New computational architectures for pricing derivatives

The problem that concerns us is the cost-effective computation of the expected value of a derivative security. One should not separate the method of computing the expected present value of a structured security from its ultimate computing topology. In particular, the network infrastructure is as least as important a factor in cost-effective computing as the algorithm design and its processor implementation. We investigate the network issues involved with deploying sophisticated derivative analytics on a modern computer network. We show that same technology that can be used to exploit parallelism can also be used to deploy sophisticated analytics to authorized users in a cost-effective way that is secure, easily updatable and relatively machine-independent. We put these ideas to practice by extending our derivative computation system, which was used to compare the derivative valuations on various computing network architectures. The benchmark problem computes an American "put" option under various interest rate scenarios using a combination of binomial lattice and Monte Carlo methods. We rebuilt the system as an executable derivative calculator applet. It is currently viewable on any Java-enabled Web Browser on the World Wide Web, independent of the computer processor or operating system. It also exploits parallelism: it uses any processor available on its local host to automatically speed itself up.